Low-cost IMUs are subject to multiple error sources, including the ones coming from mechanical, electronic, and signal processing imperfections.
To date, most existing methods on reducing the noises in IMU readings and compensating for the intrinsic models are based on statistical approaches. Standard methods in filtering the IMU readings in inertial navigation are to eliminate or attenuate the high frequency components [1]}. Auto regressing and moving average (ARMA) methods are also widely applied in this field [2]}. In addition to noises, IMU readings are also affected by sensor biases, which are generally modeled as additive components in the measurement equations [3]}. For high-end IMUs, modeling noises and biases might be enough for high-precision navigation [4]}. However, to have high-quality performance, additional parameters must be considered for low-cost IMUs, including scale factors, misalignment parameters, G-sensitivity matrix, and other high order terms [5]}, [6]}. Compensating for the thermal effects is another important factor that is widely studied and must be taken into consideration in real applications [7]}.
Once readings from IMUs are properly filtered and a closed-form sensor model equation is given, IMU poses can be straightforwardly computed by numerical integration following the Newton's law [4]}, [9]}.
